FGPO: Feasibility-Gated Preference Optimization for Video Super-Resolution

Generative video super-resolution has achieved remarkable progress in generating perceptually realistic videos. However, existing methods still face key challenges: they struggle to reconcile global structural fidelity with ambiguous local detail recovery, often yielding over-smoothed textures or geometric distortions. Moreover, preference- and reinforcement learning-based approaches lack spatially differentiated supervision, increasing the risk of structural artifacts while offering limited guidance for detail synthesis. To address these issues, we propose FGPO, a feasibility-gated preference optimization framework for video super-resolution. FGPO first integrates multi-objective evaluation and feasibility-gated Pareto selection, utilizing LR-conditioned detail plausibility and temporal consistency scores to restrict candidate eligibility and prevent reinforcing low-fidelity outputs. It then performs LR-guided spatiotemporal dispersion estimation and mask generation, aligning candidate dispersion with observable LR structures to isolate regions with high uncertainty. Finally, a dispersion-weighted inter-candidate feature diversity bonus is introduced to encourage localized detail variation in high-dispersion areas while preserving low-dispersion LR-aligned structures. FGPO provides update-rule-compatible scalar and pairwise feedback, enabling seamless compatibility with various post-training algorithms. Extensive experiments demonstrate that FGPO achieves state-of-the-art performance across reconstruction fidelity, temporal consistency, and perceptual quality, consistently outperforming existing approaches on multiple benchmarks.